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1 June 2005 Subspace selection for subpixel detection of 3D objects in hyperspectral imagery
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Abstract
Object detection in hyperspectral imagery benefits from the large amount of spectral information. The effective use of this information is crucial for a detection algorithm to achieve high accuracy under challenging conditions. In this paper, we establish subspace representations for 3D objects and backgrounds to improve discriminability for 3D detection invariant to unknown illumination and atmospheric conditions. Residual variance information is utilized to generate background and mixed residual statistics which improve the separation of target and background for detection. A new detection algorithm that uses these statistics in conjunction with a likelihood ratio test is proposed for the subpixel detection of complex 3D objects in cluttered backgrounds. Other existing algorithms, e.g. the generalized likelihood ratio test (GLRT), can be derived from this algorithm by introducing the appropriate assumptions. The new detection algorithm is evaluated for a number of images simulated using DIRSIG and also compared with other detection algorithms. The experimental results demonstrate accurate performance on these data sets.
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Yong Liu and Glenn Healey "Subspace selection for subpixel detection of 3D objects in hyperspectral imagery", Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); https://doi.org/10.1117/12.603279
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